Complexity metrics for 3D meshes
mesh_intro.Rmdhabtools includes a wide range of complexity metrics applicable to 3D meshes.
Before, calculating any metrics, visualize your mesh and make sure that the z orientation is correct, as this may affect some of the calculations.
plot3d(mcap)
Depending on how the mesh was generated (e.g. with the use of a laser scanner), the resolutions (distance between vertices inside the mesh) can vary a lot. This may affect calculations such as fractal dimension. Check the distribution of resolution of your object and if needed, remesh to make the resolution more uniform.
resvec <- Rvcg::vcgMeshres(mcap)[[2]] # vector of resolutions
hist(resvec)
summary(resvec)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.001307 0.005265 0.007003 0.007831 0.009410 0.043981In our example, the ‘mcap’ object has very variable distances between
vertices. We can solve this issue by remeshing the object with the Rvcg
function vgcUniformRemesh(). Here we set the resolution
(voxelSize) to the minimum distance between points in the original mesh
to ensure we don’t loose details. This choice may be made on a
case-to-case basis. Setting multisample to TRUE improves the accuracy of
distance field computation, but slows down the calculation so this
choice may be defined by computing power and the size of your object.
The remeshed object now has a mean resolution of approximately the
minimum of ‘resvec’. While there will still be some variation in the
obtained distances between vertices, the variation will be much smaller.
An alternative option would be to remesh using an external 3D software
such as blender.
mcap_uniform <- Rvcg::vcgUniformRemesh(mcap, silent = TRUE, multiSample = TRUE, voxelSize = min(resvec), mergeClost = TRUE)
plot3d(mcap_uniform, col = "grey")